ICES mar. Sei. Sym p., 197: 141-148. 1993

The importance of the DOC pool for primary production estimates

George A. Jackson

Jackson, G. A. 1993. The importance of the DOC pool for primary production estimates. - ICES mar. Sei. Symp.. 197: 141-148.

Phytoplankton release of dissolved organic matter has important consequences for the planktonic ecosystem. Their effects can be compared with those expected at the rates of dissolved organic carbon (DOC) release measured in field incubations. Calculated DOC release rates must be consistent with other incubation measurements, such as those for bacterial growth and for nitrate and ammonia uptake. In this paper, comparisons are made between these different sets of measurements, showing that there is no need for DOC excretion of more than 0.1 of net primary production to account for ecological effects. Furthermore, a simple model of DOC release during productivity measurements shows that grazing on phytoplankton and on bacteria during an incubation can also be important sources of fixed C loss.

George A. Jackson: Department o f Oceanography, Texas A& M University, College Station, T X 77843, USA.

Introduction

In no other part of oceanography have there been as many reversals of opinion, doubts about techniques, and uncertainties about concentrations and rates as there have been associated with dissolved organic matter (DOM) and its constituents dissolved organic carbon (DOC) and nitrogen (DON). The situation has not been helped by the fact that DOM is a mixture of chemical compounds, most of which have not been identified.

Prior to about 1975, oceanic benthic regions were believed to use DOM to meet their metabolic needs (e.g. Craig, 1969; Menzel and Ryther, 1970). With the realization that there is rapid vertical transport of or­ganic matter in relatively large, rapidly sinking particles such as fecal pellets (e.g. McCave, 1975; Deuser et al.,

1981) and, more recently, marine snow, there was a loss of general interest in DOM. The recent development of high temperature combustion techniques for measuring DOC and DON (Suzuki et al., 1985; Sugimura and Suzuki, 1988) and the resulting associations that they observed with apparent oxygen utilization and nitrate concentrations have brought DOM back to the attention of the general oceanographic community.

Higher concentrations of D O C and DON would re­quire profound changes in our understanding of the distributions and fluxes of most biologically active ele­ments (e.g., Jackson, 1988; Toggweiler, 1989). Recent

characterization of DOM has shown it to be quite different in concentration and composition from that indicated by Sugimura and Suzuki (1988) and Suzuki et

al. ( 1985) (Benner et a l . , 1992). There remains consider­able uncertainty about the real concentrations of DOC and D ON, but the area remains an active and exciting part of chemical oceanography (J. Hedges, in press).

DOM excretion by phytoplankton is important to the understanding of algal physiology (e.g., Antia et al.,

1991). Williams (1990) has given a thoughtful overview of phytoplankton DOM excretion and its ecological consequences. Excretion affects estimates of primary production rates and of nutrient uptake and is poten­tially important as a source of DOM for the large DOC and DON pools.

Much of the biological interest in DOM has resulted from the realization that bacteria consume it at rates that are high relative to those measured for primary pro­duction (e.g. Azam etal., 1983). The practice of present­ing bacterial production rates as a fraction of primary production has tended to emphasize the role of ex­cretion by algal cells, even to the point of suggested elaborate interactions between chemotactic bacterial and algal cells (e.g., Azam and Ammerman, 1984). There are other possible sources. Williams (1981) noted that large bacterial growth rates suggested DOM release by zooplankton. Jumars et al. (1989) refocused interest on zooplankton and their feces as DOM sources by

142 G. A. Jackson I C E S m a r . Sei. S y m p . , 197 (1W 3)

noting that an animal's complete adsorption of dissolved organic matter produced during digestion of its food is neither practical nor desirable. Other potential sources of DOM include the dissolution of detritus.

The measurement of phytoplankton uptake of nitro­gen compounds could be affected by DON release. Uptake is usually measured by the accumulation of material containing 15N in the particulate phase after a timed incubation. Unfortunately, much of the l5N tracer cannot be found after the incubation. This has been observed when the compounds were ammonia (Glibert et al., 1982), urea (Hansell and Goering, 1989), and nitrate (Ward eta l. , 1989). If the missing tracer is taken up, incorporated into organic compounds, and released, it can represent an important part of the tracer uptake which is not included in the uptake calculations. Bronk and Glibert (1991) have recently developed a technique of measuring DON excretion by using 15N tracer tech­niques. A comparison of estimated DON release rates yielded values ranging from 54 to 260% of estimated ammonia uptake rates. These large values suggest that technique cannot yet provide the information needed to determine the fate of the missing l5N tracer.

Sharp (1977) noted that methods commonly used to assess DO C production were subject to several artifacts which could give artificially high rates of DOC ex­cretion. Typical excretion rates were calculated from measurements of radioactivity present in the filtrate after algae had been incubated with l4C bicarbonate and removed by filtration and residual 14C bicarbonate removed by sample acidification and bubbling. Incom­plete removal, either of the phytoplankton particulate material because of cell rupture during filtration or of the 14C bicarbonate because of too short a bubbling period, would cause the measurement of radioactivity that had not been excreted by the algal cells. Today, we would add algal cells small enough to pass through the filter as another potential artifact. The presence of such large artifacts led Sharp to conclude that the evidence for extensive D O C excretion was weak.

More recent work on D O C excretion has addressed these issues and provided measurements of excretion rates and of DOC composition (e.g., Fogg, 1983). The composition of the excreted material is rich in sugars and other carbon-rich compounds (e.g., Mague e ta l. , 1980; Lancelot, 1983; Myklestad eta l. , 1989). Typical rates of excretion in cultures are about 10% of measured pri­mary production rates when cells grow rapidly, possibly more when the algae are nutrient limited. Excretion measured in field samples averages about 13% of pri­mary production, although reported values vary widely (Baines and Pace, 1991).

Models of how 14C incorporation and D OC excretion affect productivity measurements have focused on a two-box system consisting of phytoplankton and DOC

(e.g., Marra et al., 1981; Smith, 1982; Williams, 1990), although Williams (1990) briefly considered the effects of heterotrophs as well. These studies noted that inter­nal phytoplankton D OC pools would slow the initial release of labeled DO C during a 14C incubation.

This article examines, first, how large algal excretion needs to be consistent with other properties of plank- tonic ecosystems and, second, what the manifestation of such excretion would be in a bottle.

Studies of whole ecosystems

Observations o f D O C disappearance

Kirchman etal. (1991) have recently measured observed disappearance rates for D O C in seawater samples col­lected as part of the JGOFS program. They measured D OC concentrations in water samples several days after their collection using the technique of Sugimura and Suzuki (1988). DO C disappearance rates varied, but were as high as 0.36 d ” 1 over a day for initial DOC concentrations of 178 //M, equivalent to a decrease of DOC equal to 64,mM. Such high rates of D O C consump­tion should be matched by equally high rates of 0 2 consumption. If the 0 2 to D OC consumption ratio is 1, then the associated 0 2 concentration decrease in the environment should be 64 /<M. While simultaneous production by photosynthesis and consumption by res­piration complicates the interpretation of daily in situ

variations, there should still be large 0 2 changes, par­ticularly between night and day.

Oxygen concentrations at the JGOFS stations showed little variation during the days when Kirchman et al. collected their water. For example, dissolved oxygen concentrations at 04.25, 10.50, and 20.55 h on 25 May 1989 at 0-10 m depth were 287, 287.5, and 285.7 ,mM. The measured DOC change that day was 0.23 d equivalent to a 45 «M change in 24 h. The oxygen varied less than 1.8,«M over the course of 16.5 h, much less than the 20 /<M change which could be expected in the course of a half day. Similar results hold for the other two days that Kirchman et al. collected samples.

The fact that the measured high rates of DOC utiliz­ation are not consistent with the observed constancy of the oxygen concentrations suggests that those rates are not representative of environmental processes. Kirch­man et al. (1991) did note that bacterial properties in their samples differed from those of normal marine bacteria and that their measurements might not have been representative of the ocean. The oxygen concen­trations suggest that this was indeed the case. As a result, there is no need to invoke high rates of DOC excretion by phytoplankton to supply their observed bacterial needs.

IC E S m ar . S d . S y m p . . 197 ( 1993) The D OC pool fo r primary production estimates 143

Analysis o f ecosystem data

Estimates of organic matter excretion should be consist­ent with other measured properties of a system. These might include rates of net particulate production, bac­terial production, grazing rates for the different particle feeders, and biomass estimates. Vézina and Platt (1988) developed an approach, known as the inverse tech­nique, that allowed them to use least-squares minimiz­ation to estimate all of the flows in a planktonic food web from whatever data were available. They applied their technique to data from the English Channel and from the Celtic Sea. In both cases, they found that the DOC excretion was the minimum value that they allowed, 10% of the gross primary production.

Ducklow et al. (1989) applied a similar technique to data from warm core rings found off the East Coast of the United States but did not include DOC or DON production in detritus. Because DOC and DON feed the bacteria, this omission stopped bacteria from feeding on detrital matter. Their results showed no DON leakage by phytoplankton for two of three data sets, although 17% of total phytoplankton nitrogen uptake was excreted in the third. They found high DOC release rates during the two months when there was no DON release (20 and 54% of total primary production) and low DOC release when there was high DON release (5% of total primary production). These D OC and DON release rates are inconsistent and may result from not including detritus as an alternative source of DOM for bacterial growth.

Jackson and Eldridge (1992) used the inverse tech­nique to describe a planktonic food web off Southern California for which phytoplankton and bacterial pro­duction measurements were available. They estimated phytoplankton excretion rates at 10% of the phyto­plankton particulate production. Almost half of the D OC and DON needed to supply bacterial growth came from dissolution of detritus, with most of the rest coming from grazer excretion.

Hagström et al. (1988) reached a similar conclusion in the Mediterranean Sea, where they suggested that ex­cretion of 12% of the particulate production could sustain their measured bacterial populations.

Model studies of plankton food webs also suggest that large rates of DOC excretion by phytoplankton are not necessary to account for observed bacterial growth rates (Taylor and Joint, 1990).

Thus, analyses of field data on planktonic food webs which included bacterial growth rates have concluded that phytoplankton leakage need be only a small fraction of the organic matter consumed by bacteria (Vézina and Platt, 1988; Jackson and Eldridge, 1992). The exception was the case where the important path from detritus was not considered.

Incubation model

This model is designed to show the relationships be­tween measured quantities and planktonic processes occurring within an incubation chamber. The approach is similar to that taken in Jackson (1983) to describe the effect of zooplankton grazing of the algae on production estimates.

The typical phytoplankton production estimate is made by measuring the amount of l4C tracer collected on a filter a set time after the tracer is added to the solution as I4C bicarbonate. A typical seawater sample contains an assemblage of organisms which includes phytoplankton, bacteria, and grazers as well as DOM. The amount of newly formed particulate carbon col­lected on the filter is usually considered to represent the primary production. This ignores organic matter pro­duced and then excreted by the algae as well as grazing effects. Such organic matter is in the filtrate as DOC and, possibly, as bacterial biomass. Excreted primary production has been estimated by acidifying and then bubbling the filtrate to drive off inorganic 14C.

Because the different carbon pools - the phyto­plankton, the DOC, and the bacteria - have different concentrations and different time constants, they are affected differently by incubation conditions. The fol­lowing models show some of the relationships between total primary production and quantities measured after an incubation.

Basic m odel

Changes in the phytoplankton carbon concentration P are assumed to be determined by phytoplankton growth at a constant specific growth rate n and loss at a constant grazing rate y:

f = ^ P - y P ( i)

Similarly, the concentration of dissolved organic car­bon D is determined by phytoplankton excretion and by bacterial uptake at a rate proportional to D and bacterial concentration B:

— = a^P - bDB (2)dt

where a is the excretion rate, expressed as the fraction of the growth rate, and b is the DO C uptake rate constant. Note that excretion is not included in the definition of,«. Changes in bacterial concentrations depend on bacterial growth efficiency c and rate of bacterivory d

144 G. A. Jackson I C E S m ar . Sei. S y m p . . 197 (1993)

dB

dt= cbBD - dB (3)

Substitution of Equation (10) into Equation (5) and solving for B* yields

The amount of material produced during the incu­bation can be expressed by modifying Equations (1)-

(3):

dP*— = /<P - yP*

— = a//P - bD*B dt

dB*

dt= cbD*B - dB*

(4)

(5)

(6)

TP = (1 + a)//P dt (7)

D = = —0 b B„ ca^

B _ a// P(, _ ca^Pp

0 b D„ d

P* = P„(l - e - "')

Solving Equation (5) for D* yields

D* = D(,(l — e~bB|,t)

B* - Bn 1 cD()(eD() B0 . B 'l r ~dt cD()

For this case, total production is simply

TP = (1 + a)|<P„t

(12)

(13)

where P*, D*, and B* represent the concentrations of newly produced material in the phytoplankton, DOC, and bacteria.

The total phytoplankton production in the container TP is the total phytoplankton growth plus excretion after an incubation period of r:

The essential features of these equations can be made clearer by normalizing these equations, using P(l to normalize mass and/«“ 1 to normalize time: B'o = B()/P0, D'o = D(|/P(), P' = P/P„, B' = B/P„, and D ' = D/P„, r = ßt. Then Equations (10)—(12) become

(8)

(9)

P' = 1 - e “ r D' = D('|(l - e - a r / D ' \

B ' = B(') 1 - 1 . J o .cD,',

(14)(15)

B p — ca r/B

cD q

The amount of production retained on a filter FP depends on the ability of the filter to retain bacteria as well as algae. If the filter retains bacteria and algae and if the grazers have a negligible amount of labeled carbon, then FP = FPpb = P* + B*; if the filter retains only algae, then FP = FPp = P*. If DOC* is included, then the observed production is UP = P* + B* + D*.

Solu tions for d iffe ren t cond itions

Steady state

If the total concentrations of phytoplankton, DOM , and bacteria are constant and equal to their initial concen­trations Po, Do, and B(), as could occur if the system were at steady state, then

No grazing

If there is no grazing in the system, then y = d = 0. Integrating Equation (1) yields

P = P(,e-/a (16)

Substituting this into Equation (4) and integrating yields

P„(e^'t - 1) (17)

If the sample was at steady state when removed from the ocean, then Equation (9) can be used to eliminate b from the equations. Again using P0 and,« to normalize the equations, but now tracking the total normalized algal, DO C and bacterial concentrations Pt , D t , and By the system is described by

P | = erP' = er - 1

d D j a— aP r — D TBX

dr B0D ()

Integrating Equation (4) for P* assuming that y = n

yields

d ry

dr

d B | _

= aP4B||D,,

rD 'B r

ac

( 10)

( 11)

dr BÔDÔ

dB' _ ac dr BqD,',

D i Bt

D'B 'r

(18)(19)

(20 )

(21)

(22)

(23)

Equations (20)-(23) do not lend themselves to simple analytical solutions but can be solved numerically.

IC E S m ar . Sei. S y m p . . 197 ( 1993) The D OC pool fo r primary production estimates 145

Discussion o f model results

The simulations, expressed in the normalized forms, are functions of only five parameters: Bo, Dû, a, c, and z.

The values of the other rate constants are determined by these. Values for two of the parameters, a and c, are not very variable. As discussed above, estimates for a range from 0.1 to 0.3; estimates of c are 0.5 to 0.7 (e.g., Payne, 1970). Probably the most poorly known values are those for B(> and D('>. (Note that these are set by the different rate constants, as in Equations (8), (9).)

When bacterial concentration is small and DOC and algal concentrations the same, little of the labeled pro­duction goes to the bacteria (Fig. 1A). Labeled DOC takes longer to reach its steady-state concentration than labeled algae. However, respiration and grazer con­sumption of labeled carbon very rapidly decreases the amount of fixed carbon which can be recovered, even if the DOC is included (Fig. IB). Including bacteria has little effect on production estimates.

0.9

’S I °:?.N 3 0.6

0.5 0.4

° C 0.3Z 3 0.2

0.9

0.7 ü ÖCl c 0 .6

]u -2 0.5

S ça 0.4

0.3

0.2

UP'

2.50 0.5 1.5

X

Figure I. Normalized concentrations (A) and production esti­mates (B) as a function of normalized time r for D» = 1. Bo = 0.1, a = 0.1, and c = 0.5. This is a steady-state case where bacterial biomass is a small fraction of DOC, algal excretion rate is 10% of particulate primary production, and bacterial growth efficiency is 50%. Symbols: P' = labeled phytoplankton concentration relative to total algal concentration; D' = labeled DO C concentration relative to total algal concen­tration; B' = labeled bacterial carbon concentration relative to total algal concentration. In (B), primary production estimated from labeled algal concentration relative to actual algal pro­duction (Fp = Fp/TP); primary production estimated from the sum of labeled algal and bacterial concentrations relative to actual algal production (Fph = Fph/TP); primary production estimated from the sum of labeled algal, bacterial and DOC concentrations relative to actual algal production (UP/TP).

0.9_ C 0.8^3 O

0.7 0.6

I o-5o 0.4o 0.3° 0.2

C3 (—*

0.9

0.8

0.7

0.6

0.5

0.4

UP1

0.3

0.22.50.5

X

Figure 2. Normalized concentrations (A) and production esti­mates (B) as a function of normalized time r for D('> = 0.1, B,'i = 1, a = 0.1, and c = 0.5. This is a steady-state case where bacterial biomass is much greater than DOC, algal excretion rate is 10% of particulatc primary production, and bacterial uptake efficiency is 50%. Symbols as in Figure 1.

When bacterial and algal concentration are the same and that of DOC small, more material appears in the bacterial compartment (Fig. 2A). However, this occurs at a much lower rate than it does for the algae. There is a time-lag in the response of the bacteria as they must wait for the label to appear in the D O C pool first. The small size of the DOC pool does allow material to appear in the bacteria much sooner. Again, there is a rapid de­crease in the production estimated from any of the potential measurements (Fig. 2B).

When excretion rate is larger, a = 0.3, and bacterial uptake efficiency higher, c = 0.7, the relative size of the bacteria component increases more rapidly but it is still only half that of the algae for the relatively long time of when T = 3 (Fig. 3A). This is actually a long time. If the algae are growing at the fast rate of p = 1 d _1, this is equivalent to 3 days. At this time the measured pro­duction is about a third of the actual rate.

In the absence of grazing, algal concentrations in­crease rapidly (Fig. 4A), whereas DOC and bacterial concentrations decrease more slowly. For BÔ = 1 and D('| = 0.1, the labeled bacterial concentration increases quite slowly. The largest effect on productivity estimates is the overestimate of production relative to that of the natural population assumed to steady state (Fig. 4B).

There are three effects which lower the estimate of phytoplankton production, FPp: excretion of fixed car­bon; bacterial respiration; grazing losses. As long as

146 G. A . Jackson IC E S m ar . Sei. S y m p . . 1 97(1993)

0.9 C 0.8

^ - 2 0.70.6

i s °-5I g 0.4Z O 0.3

0.2

D'o= 0.1

= 0.3 = 0.7

■4—'c3u->

UP'

2.50.5

X

Figure 3. Normalized concentrations (A) and production esti­mates (B) as a function of normalized time r for D,', = 0.1, B« = 1, a = 0.3, and c = 0.7. This is a steady-state case where bacterial biomass is much greater than DOC, algal excretion rate is 30% of particulate primary production, and bacterial uptake efficiency is 70%. Symbols as in Figure 1.

os

UP'T3P 1*O

0 1.50.5

TFigure 4. Normalized concentrations (A) and production esti­mates (B) as a function of normalized time x for D» = 0.1. B,', = 1, a = 0.1. and c = 0.5. This is a case with no grazing in the incubation bottle but with grazing leading to a steady state in the environment. Initial bacterial biomass is much greater than DOC, algal excretion rate is 10% of particulate primary pro­duction, and bacterial uptake efficiency is 50%. Symbols as in Figure 1.

excretion is a relatively small fraction of total pro­duction, as it is even when a = 0.3, most labeled C moves through the phytoplankton. As such, a more careful consideration of grazing effects during an incubation should be at least as important as including the effects of excretion for the measurement of net primary pro­duction. In this model formulation, material lost to grazers is not trapped on filters. A more complete model would include the grazing model of Jackson (1983).

Larsson and Hagström (1979) made one of the more complete comparisons among primary production in particles larger than 3 um, which they assumed to be phytoplankton cells, particles between 0.2 and 3 ^m , which they assumed to be bacteria, and filtrate passing a 0.2 nm filter, which they assumed to be exudate. The time course of carbon appearing in the different frac­tions showed the general patterns noted above for small D,',: labeled DOC increased, but rapidly reached a maximum; labeled algae took longer before slowing its accumulation rate, and labeled bacteria increased slowly but maintained the same accumulation rate for 12 h.

There are, however, several puzzling aspects to their results. The initial increase rate for labeled D O C was about 3 ^ g C r ' I T 1 for 2 h. The similar rate for labeled algae was about 4 f ig C I-1 h ” 1. Bacterial carbon increased at the slow rate of about 0.8 /<g C T 1 h ' 1. Larsson and Hagström argued that the phytoplankton continued to leak D OC at the same rate but that the bacteria consumed the excess. If this were so, the bac­teria would have assimilated only 20% of what they consumed. This is much less than the 60% growth efficiency usually cited (Payne, 1970). The date of the experiment, 16 March 1976, was during a time when the integrated photosynthesis data show that almost no material went to non-phytoplankton material (Fig. 4, Larsson and Hagström, 1979).

Discussion

This model does not work well to reproduce fully the situation discussed by Larsson and Hagström (1979). The model may be faulty, the experiments may have artifacts, or both. Some of the model limitations have already been discussed. The experiments were flawed by not accounting for the potentially substantial contri­butions of cyanobacteria, algae small enough to slip through the filters and be considered as DOM (Larsson and Hagström, 1982). More work needs to be done.

This model does not help determine the fate of the l 5N missing in the incubations to determine the uptake rates of ammonia, nitrate, and urea. The rate at which ex­cretion accumulates in either D OC or bacteria is too slow to account for the observed discrepancies of l5N. It is possible that those substances accumulate in organ-

IC E S m ar . Sci. S y m p . . 197 (1993) The D O C poo! fo r primary production estimates 147

isms, such as cyanobacteria or bacteria, that are not trapped on the GFF filters usually used.

The importance of grazing, which determines what fraction of production is present to be measured, suggests that this could be a more important factor to be addressed than excretion. Ideally, both factors should be addressed in fashion to the approach in Jackson (1983) and Smith (1982).

Results of this model do suggest the importance of using short-term incubations to minimize the effects of respiratory and grazing loss of labeled material. If the labeled carbon in both the filtrate and the particles is measured, then there should be a minimal loss to bac­teria or grazers. The definition of short incubations, as in Jackson (1983), is in terms of /ut.

This desire for short incubations contrasts with Smith's (1982) conclusion that short incubation times were not good for estimating net primary production or D O C release rates because of time-lags introduced by isotopic equilibration of internal carbon pools. Measurements of net primary production must manage to be long enough for the phytoplankton to equilibrate with the isotope but short enough so that ecological interactions will not significantly alter concentrations. At best, measurements of complex systems are compro­mises.

Conclusions

Environmental data show no need for phytoplankton excretion rates much greater than 10% of net particulate primary production.

Models of the effect of excretion and resulting ecosys­tem processing on calculated production rates show that the least biased measurements are the shortest. Physio­logical considerations argue for longer measurements. The best relationship between measured and environ­mental production depends on the effects of grazing or non-grazing as well as on DOC excretion.

Acknowledgments

This work was supported by Office of Naval ResearchContract N00014 87-K0005 and US Department ofEnergy grant DE-FG05-85-ER60341.

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